The technique of training agents on wide distributions of environments to obtain better and more robust skills, called domain randomization, has also been used in single-agent settings [1]}, [2]}, [3]}, [4]}, [5]}. A growing number of recent works have designed automatic environment curricula that adapt the environment distribution over the course of training to maximize the fraction of learnable environments [6]}, [5]}, [8]}, [9]}, [10]}, [11]}, [12]}.
